Git Deal Flow
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VC Deal Flow Signal monitors GitHub engineering activity across thousands of startups and surfaces the ones showing unusual acceleration โ weeks before they hit your inbox.
We track commit velocity, contributor growth, and repository expansion to rank startups by engineering momentum. This is a leading indicator for seed and Series A investors.
What you get: - Weekly ranked reports of breakout startups across 20 sectors - Real GitHub acceleration data (not vanity metrics) - Filter by sector, stage, and geography - Live dashboard with 100+ startups tracked
Who it's for: Angel investors, VCs, and fund analysts looking for deal flow signals that aren't in everyone else's pipeline.
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Git Deal Flow's answer
We use GitHub engineering activity as a leading indicator for investors. While competitors like Harmonic, Dealroom, and Crunchbase rely on funding announcements, job postings, and web traffic, we track commit velocity, contributor growth, and repository expansion - signals that appear weeks before a startup shows up on anyone's radar. The data is public but nobody else packages it for investors.
Git Deal Flow's answer
Most deal flow tools show you what already happened - a round closed, a hire was made. We show you what's happening right now in the codebase. Engineering acceleration has historically preceded fundraise announcements by 3-6 weeks. That's the difference between setting terms and chasing a deal everyone already knows about.
Git Deal Flow's answer
Angel investors, seed and Series A VCs, fund analysts, and scout networks looking for data-driven deal sourcing. Anyone who wants to find breakout startups before consensus forms around them.
Git Deal Flow's answer
I watched a company's commit graph spike and three weeks later they announced a Series A. The signal was right there - public, free, updating in real time. Nobody was reading it. Quant funds have known for years that public data read correctly is the best leading indicator. The problem was that nobody built the lens for investors. So I did.
Git Deal Flow's answer
GitHub API for data collection, Next.js for the dashboard, Vercel for hosting, and custom algorithms for detecting acceleration patterns across thousands of startup GitHub organizations.
Git Deal Flow's answer
Based on our record, TensorFlow should be more popular than Git Deal Flow. It has been mentiond 8 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Quick context: I run GitDealFlow, an MCP server + dataset that tracks GitHub commit-velocity signals across ~100 venture-backed startups. Six free read-only tools, ~700 npm downloads in the first three weeks, listed on Glama and the official MCP registry. - Source: dev.to / 3 months ago
VC Deal Flow Signal monitors GitHub engineering activity across startup organizations and surfaces the ones showing unusual acceleration. The hypothesis: engineering acceleration (measured as the rate of change in commit velocity) is a leading indicator for fundraise announcements, usually by 6 to 12 weeks. - Source: dev.to / 3 months ago
The open-source movement offers hope here. Projects like Hugging Face are democratizing access to state-of-the-art models, while initiatives like Google's TensorFlow provide powerful frameworks without licensing costs. But even open-source solutions require technical expertise that many lack. - Source: dev.to / 4 months ago
Converting the images to a tensor: Deep learning models work with tensors, so the images should be converted to tensors. This can be done using the to_tensor function from the PyTorch library or convert_to_tensor from the Tensorflow library. - Source: dev.to / over 3 years ago
So I went to tensorflow.org to find some function that can generate a CSR representation of a matrix, and I found this function https://www.tensorflow.org/api_docs/python/tf/raw_ops/DenseToCSRSparseMatrix. Source: about 4 years ago
Can anyone offer up an explanation for why there is a performance difference, and if possible, what could be done to fix it. I'm using the installation guidelines found on tensorflow.org and installing tf2.7 through pip using an anaconda3 env. Source: about 4 years ago
I don't have much experience with TensorFlow, but I'd recommend starting with TensorFlow.org. Source: over 4 years ago
Harmonic.ai - Harmonic's data engine keeps 20M+ companies & 150M+ professional profiles fresh, so you can always be in the loop when a company just raised a round, just hired a CTO, or just crossed the 1M follower mark on Twitter.
PyTorch - Open source deep learning platform that provides a seamless path from research prototyping to...
DealRoom - M&A Lifecycle Management Software
Keras - Keras is a minimalist, modular neural networks library, written in Python and capable of running on top of either TensorFlow or Theano.
Forager - Fashion discounts gathered in your size
IBM Watson Studio - Learn more about Watson Studio. Increase productivity by giving your team a single environment to work with the best of open source and IBM software, to build and deploy an AI solution.
